Datasets:
File size: 1,957 Bytes
453129d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 | from __future__ import annotations
import torch
import torch.distributed as dist
import torch.nn.functional as F
from torch import Tensor
def _make_tp_fsdp_groups(n_tp: int, n_fsdp: int, rank: int):
tp_group = None
fsdp_group = None
for j in range(n_fsdp):
ranks = [j * n_tp + ii for ii in range(n_tp)]
g = dist.new_group(ranks)
if rank in ranks:
tp_group = g
for i in range(n_tp):
ranks = [jj * n_tp + i for jj in range(n_fsdp)]
g = dist.new_group(ranks)
if rank in ranks:
fsdp_group = g
assert tp_group is not None and fsdp_group is not None
return tp_group, fsdp_group
def _gather_fsdp_concat_dim0(shard: Tensor, fsdp_group, parts: int) -> Tensor:
lst = [torch.empty_like(shard) for _ in range(parts)]
dist.all_gather(lst, shard.contiguous(), group=fsdp_group)
return torch.cat(lst, dim=0)
def _gather_fsdp_concat_dim1(shard: Tensor, fsdp_group, parts: int) -> Tensor:
lst = [torch.empty_like(shard) for _ in range(parts)]
dist.all_gather(lst, shard.contiguous(), group=fsdp_group)
return torch.cat(lst, dim=1)
@torch.no_grad()
def solution(
x_local: Tensor,
W1_shard: Tensor,
W2_shard: Tensor,
W3_shard: Tensor,
n_tp: int,
n_fsdp: int,
) -> Tensor:
world_size = dist.get_world_size()
rank = dist.get_rank()
assert world_size == n_tp * n_fsdp, (
f"world_size ({world_size}) must equal n_tp * n_fsdp ({n_tp} * {n_fsdp})"
)
tp_group, fsdp_group = _make_tp_fsdp_groups(n_tp, n_fsdp, rank)
W1 = _gather_fsdp_concat_dim0(W1_shard, fsdp_group, n_fsdp)
W2 = _gather_fsdp_concat_dim0(W2_shard, fsdp_group, n_fsdp)
W3 = _gather_fsdp_concat_dim1(W3_shard, fsdp_group, n_fsdp)
x1 = x_local @ W1
x2 = x_local @ W2
z = F.silu(x1) * x2
y_partial = z @ W3
y = y_partial.clone()
dist.all_reduce(y, op=dist.ReduceOp.SUM, group=tp_group)
return y
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